Head of Machine Learning Engineering

Overview

In this senior role, you will define and drive the ML strategy for Trainline’s Core Experience pillar, shaping search, pricing, payments, and conversational AI. You’ll lead multiple ML teams, manage budgets, and collaborate with product and engineering to ship scalable ML solutions that enhance the customer journey. The role blends strategic leadership with hands-on delivery, including governance and ML platform improvements. This is an opportunity to impact how millions of travelers experience sustainable, value-driven travel through advanced AI.

Pay / Benefits

• private healthcare & dental insurance
• work from abroad policy
• 2-for-1 share purchase plans
• EV Scheme to reduce carbon emissions
• extra festive time off
• family-friendly benefits

Responsibilities

• Set the vision and budget for ML across the Core Experience pillar, balancing short-term delivery with long-term platform investments
• Lead multiple ML teams through their managers, coaching and hiring to foster technical excellence and delivery
• Drive end-to-end ML delivery for search relevance, pricing models, fare optimisation, and conversational AI
• Define SLIs/SLOs and operational practices to raised ML production reliability and incident management
• Shape AI governance, guardrails, and evaluation frameworks for modern AI including LLM-based features and agentic systems
• Co-own the operating model for AI at Trainline, including data privacy, security, and audit accountability
• Collaborate with Product, Engineering, Data, Analytics, and commercial teams to identify high-value ML opportunities

Key requirements

• Significant experience building and leading production ML teams (manager-of-managers)
• Proven track record shipping ML systems with measurable business/customer impact (ranking, recommendation, forecasting, optimisation, real-time decisioning)
• Deep understanding of the full ML lifecycle and strong software/platform instincts (MLOps tooling and practices)
• Experience establishing operational standards: SLIs/SLOs, monitoring, incident management, production reliability
• Budget ownership including infrastructure costs and vendor oversight
• Strong stakeholder and communication skills across strategic and technical levels
• Experience hiring, mentoring, and developing technical talent at multiple levels

• leadership
• stakeholder management
• communication
• ML lifecycle and MLOps
• AWS cloud infrastructure
• Python and ML libraries (scikit-learn, NumPy, Pandas, LightGBM)

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